The invention relates to a method for operating measuring equipment for detecting at least one analyte in a bodily fluid, a computer program with program code for carrying out the method, and to measuring equipment for detecting at least one analyte in a bodily fluid, which is designed to carry out the method according to the invention. Such methods and devices are used, in particular, in medical technology in order to monitor, either continuously or discontinuously, one or more analytes in bodily fluids such as blood, interstitial fluid or other types of bodily fluid, for example at home or in care homes or hospitals. In particular, the method can be used for operating measuring equipment with at least one continuously measuring blood glucose sensor. Such sensors, by means of which it is possible to carry out so-called continuous monitoring, are generally implanted into fatty tissue or interstitial tissue of a user for a number of days in order to then generate measurement signals, for example at regular or irregular time intervals, from which the concentration of the at least one analyte can be deduced. The at least one analyte can, in particular, be glucose, for example blood glucose. However, in general, applications other than the aforementioned applications and the applications described below are also possible.
Since these are implanted sensors, the systems must be calibrated using already established measurement methods. Treating the error of the reference system and establishing the calibration data “in the field”, i.e., without a norm, are therefore a particular challenge for these systems.
Measuring equipment for detecting at least one analyte in a bodily fluid is generally based on one or more physical and/or chemical measurement principles, by means of which one or more measurement signals are generated accordingly. By way of example, these measurement principles may be electrochemical measurement principles, by means of which one or more analyte concentrations can be detected. Such electrochemical measurement principles are known from the prior art.
However, a problem with such devices lies in the fact that, at first, the measurement signals generally are without physiological meaning. By way of example, the measurement signals may be simple currents, measured in, e.g., milliampere or nanoampere. In order to obtain information that can be used physiologically from these measurement signals, these measurement signals need to be converted into a corresponding analyte concentration by means of a suitable conversion prescription. This conversion prescription, which may for example be stored in a data processing device, is generally also referred to as calibration.
In order to apply the conversion prescription, characteristic variables (parameters) are generally required. Not all of these can be defined in advance since the sensor is implanted by the patient and hence the measurement surroundings are not precisely defined. Therefore it is necessary to carry out comparison measurements during the measurement so that the measurement signals can be very accurately converted into the analyte concentration, with no absolute norm or reference being available.
The concentration of the at least one analyte is determined in advance during a comparison measurement by means of one or more reference methods. In the following text, the disclosure will be made with reference to blood glucose as the analyte. However, alternatively or in addition thereto, it is possible to determine other types of analytes. By way of example, when determining blood glucose, the blood glucose can be determined directly by means of a chemical detection method and/or by means of another type of reference measurement, the calibration of which is already known. The measurement signals from the measuring equipment are then related to the reference values of the reference measurements for the calibration. By way of example, these can be measured current curves of continuously measuring glucose systems (continuous monitoring systems), which are related to blood glucose measurements that are measured in another way, for example individual measurements using test strips. This known relationship, which is included in a corresponding calibration, can then be used to deduce a concentration of the analyte in the bodily fluid during future measurements from measurement signals from the measuring equipment. It should be noted here that the reference measurements also have a non-negligible measurement error.
H. Passing and W. Bablok: A New Biometrical Procedure for Testing the Equality of Measurements from Two Different Analytical Methods: Application of linear regression procedures for method comparison studies in Clinical Chemistry, part I, Journal of Clinical Chemistry Clinical Biochemistry, Vol. 21, 1983, pages 709-720, have, in general, disclosed a biometric method for checking the equality of measurement values from two analytical methods. Here the use of linear regression methods in method comparison studies in clinical chemistry is described retrospectively.
The prior art has disclosed a multiplicity of calibration methods for measuring equipment, in particular for glucose measuring equipment. Here, the following text refers in particular to measuring equipment comprising at least one continuously measuring sensor, more particularly at least one continuously measuring blood glucose sensor, without this restricting possible further applications.
Many of the known methods for calibration create a correlation between the measurement signal and the glucose profile in the blood using various standard regression methods. By way of example, this can be a linear regression, a fit according to the method of least squares or the like. An example for such linear regression methods is presented in EP 1 154 718 B1. There sampled data from a glucose monitoring device are calibrated using at least one blood glucose reference read out. More particularly, it proposes calculating calibration factors using a linear regression.
A further method for calculating a relationship between the measurement signals and the reference values lies in the use of expert systems. By way of example, these are mentioned in U.S. Pat. No. 6,326,160 B1 or in U.S. Pat. No. 6,233,471. These methods use weighted sums to create a correlation between a continuously measured current profile and a blood glucose profile.
US Publication No. 2008/0081977A1 has likewise disclosed a calibration model, which in particular also takes offset times between detecting the reference values and the measurement signals into account.
US Publication No. 2008/0021666 A1 likewise undertakes the plotting of calibration data over measured data. A regression method (in this case a least squares regression) is also undertaken in this document, and this is used to calculate a slope of a fitted straight line through the calibration points.
US Publication No. 2006/0281985A1 has disclosed a method for calibrating a biosensor for detecting an analyte. Here, a multiplicity of measurement signals from the biosensor are detected over a period of time. A median filter is applied to this multiplicity of measurement signals, and the median value obtained thus is used to establish sensor sensitivity from a comparison with a measured blood analyte concentration. Here different weightings of the sensitivities can be undertaken for the different phases.
Furthermore, there often is the problem of handling the multiplicity of data in the case of real measurements, particularly in the case of continuous measurements that are undertaken over a relatively long period of time. Data compression methods, like the ones described in, e.g., U.S. Pat. No. 7,389,133 B1 or US Publication No. 2007/0016127 A1, were developed for this purpose.
However, in terms of practical handling, the presented prior art has a multiplicity of disadvantages and technical challenges. Thus, for example, most of the described calibration methods do not, or only insufficiently, account for the occurrence of outliers. Thus, for example, the calibration may yield extraordinary calibration points, i.e., calibration points which are a long way outside of the profile or value range that is expected in accordance with the remaining calibration points, and are colloquially referred to as outliers. Although these can be discarded within the scope of plausibility analysis, this can in turn lead to a falsification of the calibration.
Methods for converting the measurement signals into interpretable analyte concentrations, for example blood glucose values, should in general reproduce the profile of the analyte concentration as precisely as possible. Moreover, these methods should be robust with respect to outliers, which are created as a result of random and systematic negative influences such as temperature, movement of the user (e.g., a patient) or similar negative influences. Nor is the influence of a measurement error in the reference measurement taken into account. The known measurements only insufficiently take account of these requirements. Here, an essential point in many cases is that the measurement signal can be strongly dependent on the location and/or positioning of the actual sensor and, e.g., in the case of implanted sensors, on insertion influences. Hence a calibration in advance, as is conventional in test-strip systems, is generally unavailable.
In order to calibrate the measurement signals from continuously measuring sensors, use is therefore often made of sporadic, e.g., spaced apart by twelve hours, spot measurements, in which the calibration is for example carried out with respect to one or more reference values measured by means of test-strip equipment. However, this procedure harbors a number of challenges for the calibration. Thus, for example, although a blood glucose level, as can be measured by spot monitoring systems, and an interstitial glucose level, as can be measured by e.g., a continuously measuring, implanted sensor, are strongly correlated, they initially are two different measurement objects. In particular, dead times, which are not necessarily constant, may occur between the blood glucose profile and the interstitial glucose profile. Moreover, the blood glucose values used for the measurement can hardly be monitored in practice. While normal calibration methods have prescriptions in respect of the covered concentration range, here there often is a dependency on processing the concentration ranges prescribed by the patient. Accordingly, it may be that the concentration range, within which a calibration can be carried out, is comparatively small. Later measurements may be correspondingly imprecise if measurement signals occur outside of the calibrated range. Furthermore, the number of available calibration points is severely restricted in practice, and these calibration points harbor great uncertainty since these are generally carried out not by means of reference measurements under laboratory conditions, but by means of reference measurements using simple, everyday blood glucose equipment and under undefined measurement conditions. Likewise, the complete number of calibration points is generally only available at the end of the measurement time. However, the user generally wishes to be informed at the earliest opportunity in respect of the profile of his glucose level.